基于改进的Elman神经网络的中长期径流预报  

Medium-and Long-Term Runoff Forecasting Based on Improved Elman Neural Network

作　　者：屈亚玲[1] 周建中[1] 刘芳[1] 杨俊杰[1] 李英海[1] 

机构地区：[1]华中科技大学水电与数字化工程学院,湖北武汉430074

出　　处：《水文》2006年第1期45-50,共6页Journal of China Hydrology

摘　　要：径流中长期预报长期以来一直都是人们关注的热点研究问题。现行的径流预报方法很多,传统的有时间序列法,多元回归分析法等,这些方法虽然简单易用,但是如果预报对象提供的样本容量偏小或者因子选择不够合理,都会造成预报精度偏差过大,难于有效的指导工程应用。鉴于此,本文提出一种改进的采用局部回归的Elman神经网络方法。并应用到凤滩水库优化调度的径流预报中。结果表明,与回归分析法、BP网络相比较,该方法不仅提高了算法的效率,而且提高了预报的精度,在径流预报中具有有效性和优越性。There are many methods for medium- and long-term runoff forecasting, such as the traditional methods of time series, multiple linear regression and etc.. Although this kind of methods are easy to use, they often have deviation in forecasting precision if the forecasting objects supply fewer samples or the factors are unreasonably chosen. This paper introduces an improved Elman neural network, which has been used in the runoff forecasting for optimized regulation of the Fengtang Reservoir. The improved Elman network has been compared with regression procedure and BP network. The results show that the method can not only raise calculating efficiency, but also increase forecasting precision.

关 键 词：中长期径流预报 多元回归 BP网络 改进的Elman神经网络 

分 类 号：P338.9[天文地球—水文科学]